get_events_for_detail_generation

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the response structure (prompt text + event documents) and the sorting priority order. As a read operation implied by 'get', no side effects are mentioned but none are expected. It lacks explicit read-only confirmation, but the name and content cover the essentials.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-sentence purpose, a note on the response payload with a usage instruction, and a clear priority order. Every sentence earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return values are documented. The description explains the selection criteria (priority order) and how to use the results (feed both prompt and events to LLM). This is complete for a retrieval tool with two simple parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both 'skip' and 'batch_size' fully described. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action: 'Return events needing AI-generated detail pages'. This distinguishes it from sibling tools like get_events (general listing) and get_contests_for_detail_generation (contests, not events). The priority order further clarifies the scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: it is for fetching events that require AI-generated detail pages, and it instructs to send both prompt text and event documents to the LLM. It does not explicitly name alternatives or exclusions, but the use case is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.